Accurate crowd density estimation has become critical in applications ranging from intelligent urban planning and public safety monitoring to marketing analytics and emergency response. In recent developments, various methods have been used to enhance the precision of crowd analysis systems. In this study, a Convolutional Neural Network (CNN)-based approach was presented for crowd density detection, wherein the Congested Scene Recognition Network (CSRNet) architecture was employed with a Visual Geometry Group (VGG)-16 backbone. This method was applied to two benchmark datasets—Mall and Crowd-UIT—to assess its effectiveness in real-world crowd scenarios. Density maps were generated to visualize spatial distributions, and performance was quantitatively evaluated using Mean Squared Error (MSE) and Mean Absolute Error (MAE) metrics. For the Mall dataset, the model achieved an MSE of 0.08 and an MAE of 0.10, while for the Crowd-UIT dataset, an MSE of 0.05 and an MAE of 0.15 were obtained. These results suggest that the proposed VGG-16-based CSRNet model yields high accuracy in crowd estimation tasks across varied environments and crowd densities. Additionally, the model demonstrates robustness in generalizing across different dataset characteristics, indicating its potential applicability in both surveillance systems and public space management. The outcomes of this investigation offer a promising direction for future research in data-driven crowd analysis, particularly in enhancing predictive reliability and real-time deployment capabilities of deep learning models for population monitoring tasks.
A novel electronic voting system (EVS) was developed by integrating blockchain technology and advanced facial recognition to enhance electoral security, transparency, and accessibility. The system integrates a public, permissionless blockchain—specifically the Ethereum platform—to ensure end-to-end transparency and immutability throughout the voting lifecycle. To reinforce identity verification while preserving voter privacy, a facial recognition technology based on the ArcFace algorithm was employed. This biometric approach enables secure, contactless voter authentication, mitigating risks associated with identity fraud and multiple voting attempts. The confluence of blockchain technology and facial recognition in a unified architecture was shown to improve system robustness against tampering, data breaches, and unauthorized access. The proposed system was designed within a rigorous research framework, and its technical implementation was critically assessed in terms of security performance, scalability, user accessibility, and system latency. Furthermore, potential ethical implications and privacy considerations were addressed through the use of decentralized identity management and encrypted biometric data storage. The integration strategy not only enhances the verifiability and auditability of election outcomes but also promotes greater inclusivity by enabling remote participation without compromising system integrity. This study contributes to the evolving field of electronic voting by demonstrating how advanced biometric verification and distributed ledger technologies can be synchronously leveraged to support democratic processes. The findings are expected to inform future deployments of secure, accessible, and transparent electoral platforms, offering practical insights for governments, policymakers, and technology developers aiming to modernize electoral systems in a post-digital era.
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